Short-term Memory Neural Network Architecture Research Articles

Deep learning-based algorithm accurately classifies sleep stages in preadolescent children with sleep-disordered breathing symptoms and age-matched controls.

Visual sleep scoring has several shortcomings, including inter-scorer inconsistency, which may adversely affect diagnostic decision-making. Although automatic sleep staging in adults has been extensively studied, it is uncertain whether such sophisticated algorithms generalize well to different pediatric age groups due to distinctive EEG characteristics. The preadolescent age group (10-13-year-olds) is relatively understudied, and thus, we aimed to develop an automatic deep learning-based sleep stage classifier specifically targeting this cohort. A dataset (n = 115) containing polysomnographic recordings of Icelandic preadolescent children with sleep-disordered breathing (SDB) symptoms, and age and sex-matched controls was utilized. We developed a combined convolutional and long short-term memory neural network architecture relying on electroencephalography (F4-M1), electrooculography (E1-M2), and chin electromyography signals. Performance relative to human scoring was further evaluated by analyzing intra- and inter-rater agreements in a subset (n = 10) of data with repeat scoring from two manual scorers. The deep learning-based model achieved an overall cross-validated accuracy of 84.1% (Cohen's kappa κ = 0.78). There was no meaningful performance difference between SDB-symptomatic (n = 53) and control subgroups (n = 52) [83.9% (κ = 0.78) vs. 84.2% (κ = 0.78)]. The inter-rater reliability between manual scorers was 84.6% (κ = 0.78), and the automatic method reached similar agreements with scorers, 83.4% (κ = 0.76) and 82.7% (κ = 0.75). The developed algorithm achieved high classification accuracy and substantial agreements with two manual scorers; the performance metrics compared favorably with typical inter-rater reliability between manual scorers and performance reported in previous studies. These suggest that our algorithm may facilitate less labor-intensive and reliable automatic sleep scoring in preadolescent children.

Assessing the Soldier Survivability Tradespace Using a Single IMU

Soldier burden is influenced by the environment, metabolic demands, equipment properties, and psychological stressors; however, much of our knowledge of soldier burden is in the context of body-borne load mass in controlled laboratory environments. Thus, to further our understanding of how all aspects of soldier burden affect the survivability tradespace (i.e., performance, health, and susceptibility to enemy action), field-based motion capture methods are needed. We developed a human activity recognition method using the deep convolutional long short-term memory neural network architecture, trained using a single inertial measurement unit on the upper back, to identify eleven tactical movement patterns commonly performed by soldiers. Using a two-step logical algorithm, real-world constraints are forced, and class labels are expanded to 19 movements. Presented are three models based on Indoor, Section Attack (outdoors), and a General approach. Across all three approaches, we obtained an average accuracy of 90.0%. Further, we used these predictions to calculate meaningful tradespace metrics, which had an excellent agreement with calculations using the true labels. Military leaders and defence scientists can use this approach to quantify tradespace metrics in the field, as a preprocessing tool to supplement other technology, and make data-driven decisions that can help improve performance, decrease susceptibility, and increase overall mission success.

P137 Deep learning enables accurate automatic sleep stage classification in a clinical paediatric population

Abstract Introduction Sleep disorders are widespread in children and associated with a myriad of detrimental health sequelae. Accurate identification of sleep stages is crucial in diagnosing various sleep disorders; however, manual sleep stage scoring can be subjective, laborious, and costly. To tackle these shortcomings, we aimed to develop an accurate deep learning-based approach to automate sleep staging in a paediatric cohort. Methods A clinical dataset (n=115, 35% girls) containing overnight polysomnographic recordings of 10–13-year-old Icelandic children from the EuroPrevall-iFAAM study was utilised to develop a combined convolutional and long short-term memory neural network architecture. A three-channel input comprising electroencephalography (F4-M1), electrooculography (E1-M2), and chin electromyography was used to train and evaluate the model to classify sleep into five stages (wake/N1/N2/N3/REM) using 10-fold cross-validation. Further, inter-rater reliabilities between two manual scorers and the automatic method were investigated in a subset (n=10) of the population. Results The automatic classification model achieved an accuracy of 84.5% (Cohen’s kappa κ=0.78: substantial agreement with manual scorings). Inter-rater reliability attained between two manual scorers was 84.6% (κ=0.78), and the automatic method achieved similar concordances with them, 83.4% (κ=0.76) and 82.7% (κ=0.75). Discussion The developed model achieved high accuracy and compared favourably to previously published state-of-the-art methods (performance range: 74.8%-84.3%). Inter-rater reliabilities were on par with the consensus between manual scorers and even better than among international sleep centres (commonly 0.57–0.63 as per literature). Therefore, incorporating the proposed methodology in clinical practice could be highly beneficial as it enables fast, cost-effective, and accurate sleep classification in children.

System for Neural Network Determination of Atrial Fibrillation on ECG Signals with Wavelet-Based Preprocessing

Today, cardiovascular disease is the leading cause of death in developed countries. The most common arrhythmia is atrial fibrillation, which increases the risk of ischemic stroke. An electrocardiogram is one of the best methods for diagnosing cardiac arrhythmias. Often, the signals of the electrocardiogram are distorted by noises of varying nature. In this paper, we propose a neural network classification system for electrocardiogram signals based on the Long Short-Term Memory neural network architecture with a preprocessing stage. Signal preprocessing was carried out using a symlet wavelet filter with further application of the instantaneous frequency and spectral entropy functions. For the experimental part of the article, electrocardiogram signals were selected from the open database PhysioNet Computing in Cardiology Challenge 2017 (CinC Challenge). The simulation was carried out using the MatLab 2020b software package for solving technical calculations. The best simulation result was obtained using a symlet with five coefficients and made it possible to achieve an accuracy of 87.5% in recognizing electrocardiogram signals.

Social graph convolutional LSTM for pedestrian trajectory prediction

Understanding the movement of pedestrians and predicting their future trajectory can be very important in intelligent transportation systems because accurate pedestrian trajectory prediction will improve the level of autonomous driving technology and reduce traffic accidents. The authors address this problem with a social graph convolutional long short-term memory neural network architecture by considering the movement information of each pedestrian and its interaction with neighbours. Specifically, the authors use a graph to model pedestrian walking state where nodes denote the pedestrian movement information, and edges represent the interactions between pairwise pedestrians. An end-to-end architecture that combines a sequence-to-sequence model with a graph convolutional network to learn the movement features and interaction features is used. To capture the interaction influence on different pedestrians, an emotion gate to refine the learned features and filter out useless information is introduced. A companion loss function to increase the ability of the network to capture ‘walking in groups’ behaviour is further proposed. Through experiments on two public datasets (ETH and UCY), the authors prove that our method outperforms the previous methods.

Memory embedded non-intrusive reduced order modeling of non-ergodic flows

Generating a digital twin of any complex system requires modeling and computational approaches that are efficient, accurate, and modular. Traditional reduced order modeling techniques are targeted at only the first two, but the novel nonintrusive approach presented in this study is an attempt at taking all three into account effectively compared to their traditional counterparts. Based on dimensionality reduction using proper orthogonal decomposition (POD), we introduce a long short-term memory neural network architecture together with a principal interval decomposition (PID) framework as an enabler to account for localized modal deformation. As an effective partitioning tool for breaking the Kolmogorov barrier, our PID framework, therefore, can be considered a key element in the accurate reduced order modeling of convective flows. Our applications for convection-dominated systems governed by Burgers, Navier-Stokes, and Boussinesq equations demonstrate that the proposed approach yields significantly more accurate predictions than the POD-Galerkin method and could be a key enabler toward near real-time predictions of unsteady flows.

Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach

This study proposes a novel Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF) model that can learn hidden heterogeneous pairwise correlations between stations to predict station-level hourly demand in a large-scale bike-sharing network. Two architectures of the GCNN-DDGF model are explored; GCNNreg-DDGF is a regular GCNN-DDGF model which contains the convolution and feedforward blocks, and GCNNrec-DDGF additionally contains a recurrent block from the Long Short-term Memory neural network architecture to capture temporal dependencies in the bike-sharing demand series. Furthermore, four types of GCNN models are proposed whose adjacency matrices are based on various bike-sharing system data, including Spatial Distance matrix (SD), Demand matrix (DE), Average Trip Duration matrix (ATD), and Demand Correlation matrix (DC). These six types of GCNN models and seven other benchmark models are built and compared on a Citi Bike dataset from New York City which includes 272 stations and over 28 million transactions from 2013 to 2016. Results show that the GCNNrec-DDGF performs the best in terms of the Root Mean Square Error, the Mean Absolute Error and the coefficient of determination (R2), followed by the GCNNreg-DDGF. They outperform the other models. Through a more detailed graph network analysis based on the learned DDGF, insights are obtained on the “black box” of the GCNN-DDGF model. It is found to capture some information similar to details embedded in the SD, DE and DC matrices. More importantly, it also uncovers hidden heterogeneous pairwise correlations between stations that are not revealed by any of those matrices.